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Enterprise Data Warehouse Migration — Sybase to Amazon Redshift

Re-platforming a tier-1 banking EDW onto a cloud-native AWS warehouse

Led the migration of a major retail bank’s on-premises Enterprise Data Warehouse from Sybase to Amazon Redshift on AWS, re-engineering 800+ stored procedures and automating data-quality testing to lift processing throughput by 40%.

PythonSQLPL-SQLAmazon RedshiftAWS S3PostgreSQLSybaseSQL ServerDBeaver
Problem Statement

The bank’s on-premises Enterprise Data Warehouse (EDW) could no longer keep up. Hard ceilings on scalability and performance slowed reporting, ran up maintenance cost, and left the business behind on growing data volumes and regulatory demand.

  • A single on-prem Sybase EDW could no longer scale with data volume or query concurrency.
  • 800+ tightly-coupled stored procedures made every change slow, risky and expensive to test.
  • Manual data validation left integrity gaps that eroded trust in downstream reporting.
Headline Outcomes
+40%Redshift compute

Data-processing throughput

+30%automation

Operational efficiency

95%automated QA

Data-validation accuracy

The Solution

A phased, zero-data-loss migration from Sybase to Amazon Redshift on AWS. It re-architected 800+ stored procedures, decoupled compute from storage, and wrapped the pipeline in automated data-quality tests so correctness was checked against source, not assumed.

Re-engineered 800+ Sybase/PL-SQL stored procedures into Redshift-optimised SQL with tuned distribution and sort keys.

Staged raw extracts through AWS S3, then bulk-loaded into Redshift for elastic, pay-as-you-grow compute.

Automated data-testing harness validated every table against source, locking in 95% validation accuracy.

Decoupled storage and compute to absorb peak reporting loads without over-provisioning hardware.

System Architecture

How the data flows

01

Sybase EDW

Legacy on-prem source

02

Extract → S3

Python + SQL staging

03

Procedure Re-write

800+ procs → Redshift SQL

04

Bulk Load

Redshift COPY

05

Automated QA

95% validation accuracy

Result 01

Gave the bank elastic cloud scale for its analytics workload.

Result 02

Turned an 800-procedure migration into a repeatable, test-driven process.

Result 03

Cut manual validation effort while raising confidence in regulatory reporting.

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